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Ren Z, Easley TO, Pineda FD, Guo X, Barber RF, Karczmar GS. Pharmacokinetic Analysis of Enhancement-Constrained Acceleration (ECA) reconstruction-based high temporal resolution breast DCE-MRI. PLoS One 2023; 18:e0286123. [PMID: 37319275 PMCID: PMC10270582 DOI: 10.1371/journal.pone.0286123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 05/09/2023] [Indexed: 06/17/2023] Open
Abstract
The high spatial and temporal resolution of dynamic contrast-enhanced MRI (DCE-MRI) can improve the diagnostic accuracy of breast cancer screening in patients who have dense breasts or are at high risk of breast cancer. However, the spatiotemporal resolution of DCE-MRI is limited by technical issues in clinical practice. Our earlier work demonstrated the use of image reconstruction with enhancement-constrained acceleration (ECA) to increase temporal resolution. ECA exploits the correlation in k-space between successive image acquisitions. Because of this correlation, and due to the very sparse enhancement at early times after contrast media injection, we can reconstruct images from highly under-sampled k-space data. Our previous results showed that ECA reconstruction at 0.25 seconds per image (4 Hz) can estimate bolus arrival time (BAT) and initial enhancement slope (iSlope) more accurately than a standard inverse fast Fourier transform (IFFT) when k-space data is sampled following a Cartesian based sampling trajectory with adequate signal-to-noise ratio (SNR). In this follow-up study, we investigated the effect of different Cartesian based sampling trajectories, SNRs and acceleration rates on the performance of ECA reconstruction in estimating contrast media kinetics in lesions (BAT, iSlope and Ktrans) and in arteries (Peak signal intensity of first pass, time to peak, and BAT). We further validated ECA reconstruction with a flow phantom experiment. Our results show that ECA reconstruction of k-space data acquired with 'Under-sampling with Repeated Advancing Phase' (UnWRAP) trajectories with an acceleration factor of 14, and temporal resolution of 0.5 s/image and high SNR (SNR ≥ 30 dB, noise standard deviation (std) < 3%) ensures minor errors (5% or 1 s error) in lesion kinetics. Medium SNR (SNR ≥ 20 dB, noise std ≤ 10%) was needed to accurately measure arterial enhancement kinetics. Our results also suggest that accelerated temporal resolution with ECA with 0.5 s/image is practical.
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Affiliation(s)
- Zhen Ren
- Department of Radiology, The University of Chicago, Chicago, Illinois, United States of America
| | - Ty O. Easley
- McKelvey School of Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Federico D. Pineda
- Department of Radiology, The University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Xiaodong Guo
- Department of Radiology, The University of Chicago, Chicago, Illinois, United States of America
| | - Rina F. Barber
- Department of Statistics, The University of Chicago, Chicago, Illinois, United States of America
| | - Gregory S. Karczmar
- Department of Radiology, The University of Chicago, Chicago, Illinois, United States of America
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Fan X, Chatterjee A, Pittman JM, Yousuf A, Antic T, Karczmar GS, Oto A. Effectiveness of Dynamic Contrast Enhanced MRI with a Split Dose of Gadoterate Meglumine for Detection of Prostate Cancer. Acad Radiol 2022; 29:796-803. [PMID: 34583866 DOI: 10.1016/j.acra.2021.07.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 07/19/2021] [Accepted: 07/31/2021] [Indexed: 01/08/2023]
Abstract
RATIONALE AND OBJECTIVES To evaluate whether dynamic contrast enhanced (DCE) MRI with a split injection of 30% followed by 70% of a standard dose (30PSD and 70PSD) of gadoterate meglumine (DOTAREM) can improve diagnosis of prostate cancer (PCa). MATERIALS AND METHODS MRI for twenty patients was performed on a Philips Ingenia 3T scanner without an endorectal coil followed by subsequent radical prostatectomy. DCE 3D T1-FFE data were acquired with injection of 0.03 mmol/kg followed after 2 minutes by 0.07 mmol/kg of DOTAREM. Regions-of-interest on histologically verified PCa and normal tissue in different prostate zones and the iliac artery were drawn. Average signal intensity as function of time was calculated for each ROI and fitted by using the signal intensity form of the Tofts (SI-Tofts) model to extract physiological parameters (Ktrans and ve). In addition, the scaled arterial input function (AIF) obtained from 30PSD data was used to analyze 70PSD data. RESULTS The AIF obtained from 30PSD data showed both first and second passes clearly and had much higher peak magnitude than AIFs from 70PSD data. Ktrans was significantly (p < 0.05) larger in PCa than in normal tissue in peripheral zone (PZ) and central zone (CZ) for both 70PSD and 70PSD data analyzed with a scaled AIF. Ktrans in cancer overlapped with that of normal tissue in the transition zone (TZ). There was no statistical difference in ve between cancer and normal tissue. Receiver operating characteristic analysis showed that use of the AIF from 30PSD data to analyze 70PSD data increased the diagnostic efficacy of Ktrans in the PZ and CZ. CONCLUSION The split dose protocol for injection of Dotarem increased diagnostic accuracy of quantitative analysis with the SI-Tofts model.
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Woodall RT, Sahoo P, Cui Y, Chen BT, Shiroishi MS, Lavini C, Frankel P, Gutova M, Brown CE, Munson JM, Rockne RC. Repeatability of tumor perfusion kinetics from dynamic contrast-enhanced MRI in glioblastoma. Neurooncol Adv 2022; 3:vdab174. [PMID: 34988454 PMCID: PMC8715899 DOI: 10.1093/noajnl/vdab174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Background Dynamic contrast-enhanced MRI (DCE-MRI) parameters have been shown to be biomarkers for treatment response in glioblastoma (GBM). However, variations in analysis and measurement methodology complicate determination of biological changes measured via DCE. The aim of this study is to quantify DCE-MRI variations attributable to analysis methodology and image quality in GBM patients. Methods The Extended Tofts model (eTM) and Leaky Tracer Kinetic Model (LTKM), with manually and automatically segmented vascular input functions (VIFs), were used to calculate perfusion kinetic parameters from 29 GBM patients with double-baseline DCE-MRI data. DCE-MRI images were acquired 2-5 days apart with no change in treatment. Repeatability of kinetic parameters was quantified with Bland-Altman and percent repeatability coefficient (%RC) analysis. Results The perfusion parameter with the least RC was the plasma volume fraction (v p ), with a %RC of 53%. The extra-cellular extra-vascular volume fraction (v e ) %RC was 82% and 81%, for extended Tofts-Kety Model (eTM) and LTKM respectively. The %RC of the volume transfer rate constant (K trans ) was 72% for the eTM, and 82% for the LTKM, respectively. Using an automatic VIF resulted in smaller %RCs for all model parameters, as compared to manual VIF. Conclusions As much as 72% change in K trans (eTM, autoVIF) can be attributable to non-biological changes in the 2-5 days between double-baseline imaging. Poor K trans repeatability may result from inferior temporal resolution and short image acquisition time. This variation suggests DCE-MRI repeatability studies should be performed institutionally, using an automatic VIF method and following quantitative imaging biomarkers alliance guidelines.
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Affiliation(s)
- Ryan T Woodall
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Prativa Sahoo
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Yujie Cui
- Division of Biostatistics, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Bihong T Chen
- Department of Diagnostic Radiology, City of Hope, Duarte, California, USA
| | - Mark S Shiroishi
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Cristina Lavini
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Paul Frankel
- Division of Biostatistics, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Margarita Gutova
- Department of Stem Cell Biology and Regenerative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Christine E Brown
- Department of Hematology & Hematopoietic Cell Transplantation, Beckman Research Institute, City of Hope, Duarte, California, USA.,Department of Immuno-Oncology, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Jennifer M Munson
- Department of Biomedical Engineering & Mechanics, Fralin Biomedical Research Institute, Virginia Tech, Roanoke, Virginia, USA
| | - Russell C Rockne
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
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